Aug 2026· IoT· Vol 7, pp. 66· 0 citations· 37 references
TL;DR
These findings demonstrate that DI-MNA provides an effective balance between solution quality, scalability, and computational efficiency for resource allocation in large-scale IoT networks.
Abstract
The Multi-Node Allocation (MNA) problem in Internet of Things (IoT) networks arises when application requirements exceed the capacity of a single node, requiring job distribution across multiple devices. This problem is challenging in large-scale heterogeneous environments once it involves optimizing resource utilization, bandwidth consumption, and latency within a rapidly expanding search space. This paper proposes two scalable approaches: a greedy heuristic called Demand Index Multi-Node Allocation (DI-MNA) and a hybrid evolutionary algorithm (NSGA-Hyb) that combines DI-MNA with NSGA-III. Both methods use bounded combinatorial exploration and a normalized demand index to guide the search efficiently. The approaches are evaluated on IoT networks ranging from 10 to 1000 nodes under different workload conditions and compared with an optimal Branch and Bound (B&B) algorithm for small instances. Results show that DI-MNA achieves near-optimal solutions in small networks while maintaining low computational cost as network size grows. In large-scale scenarios, DI-MNA consistently matches or outperforms the evolutionary methods and sustains runtime speedups of up to 51× over NSGA-Hyb and more than 7.8×106 over B&B. These findings demonstrate that DI-MNA provides an effective balance between solution quality, scalability, and computational efficiency for resource allocation in large-scale IoT networks.
This work aims to design an efficient framework by incorporating a novel hybrid metaheuristic algorithm that combines Draco Lizard Optimization (DLO) and Sand Cat Optimization (SCO) for optimal task offloading and resource allocation for IoT applications.
Experiments show that the proposed Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing that spans two layers in heterogeneous cloud computing systems obtains 25-30% energy savings compared with ordinary methods, significantly reduces p95 latency and also achieves a relatively better Quality Of Service.
Eram Fatma, Nidhi Mishra, Mohammed Abdul Bari· Journal of Intelligent Decis...· 0 citations
The Internet of Things (IoT) has grown rapidly in recent years, enabling the interconnection of a large number of heterogeneous and distributed devices. This number is expected to exceed 70 billion according to Statista. With this massive scale, fulfilling complex IoT applications that require combinations of multiple objects remains a real challenge. Moreover, several Quality of Service (QoS) requirements must be satisfied, making the problem of selecting appropriate IoT services NP-hard. In such environments, task offloading is a key mechanism to efficiently distribute computational workloads across edge, fog, and cloud resources. However, selecting the optimal offloading decision remains a difficult NP-hard problem due to system heterogeneity and conflicting objectives. In this paper, we propose a GNN-DQN-based approach for task offloading in edge–fog–cloud environments. Unlike prior GNN-DQN approaches limited to single- or dual-tier architectures, our framework explicitly models heterogeneous node types and inter-tier communication links, enabling more balanced and scalable resource allocation. Experimental results show that GNN-DQN achieves a mean latency of 2.64 s, representing improvements of 70.2% over Random, 7.8% over DQN-only, and 3.5% over Greedy. A GNN-A2C baseline is also included to broaden the comparison with a modern DRL method. Despite sharing the same GNN encoder, it underperforms GNN-DQN across all metrics, confirming the superiority of the DQN learning backbone. These results highlight the effectiveness of integrating graph-based representation with reinforcement learning, while also revealing a trade-off between latency optimization and energy efficiency.
Sirine Hakim, Sonia Yassa· International Conference on...· 0 citations
Optimal placement of services is a major challenge in the context of fog computing due to the resource-constrained and heterogeneous nature of nodes. Services must be placed optimally without overloading the nodes to fulfill the increasing demand of Internet of Things (IoT) applications. Energy consumption should also be minimized, along with the provision of Quality of Service (QoS). Typical problems of existing metaheuristic approaches, such as premature convergence, limited exploration capability, and high computational cost, result in suboptimal service placement. To solve this problem, we propose a QoS-aware and energy-efficient metaheuristic optimization-based service placement strategy for an integrated IoT and fog computing environment to improve QoS. In this metaheuristic optimization-based strategy, Differential Evolution (DE) is integrated with JAYA-based exploitation, which explores the search space, which in turn accelerates convergence toward optimum service placement in fog computing environments. This proposed strategy accounts for both communication and computational energy consumption when placing service modules, thereby enhancing execution efficiency and optimizing service delay and network usage in constrained fog computing settings. The experimental results demonstrate that the developed hybrid algorithm reduces energy consumption by 3.09% compared to the existing baseline approaches, while optimizing delay and minimizing network usage. Additionally, the proposed approach has been found to minimize network usage significantly compared with baselines.
Pallavi Mettupalli Venkata, Thatikonda Supraja, Priyanka Chawla et al.· Journal of Supercomputing· 0 citations
There is an explosion in IoT devices, 5G technology, and MECs, that results in increasing demands on effective and scalable network services management. Service function chaining, defined as the sequence of functions in VNFs on a path, is one of the core principles behind the NFV architecture design. SFC allocation to the heterogeneous clouds–fogs–edges network is an NP-hard problem characterized by mutually conflicting goals, such as latency minimization, energy and cost reduction, and resource maximization. In this study, an in-depth comparison study is carried out on three population-based optimization algorithms for solving the placement problem of SFCs using Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Grey Wolf Optimization (GWO) on three cases: (1) VNF deployment cost/QoE optimization in a 5G hybrid cloud with 12 nodes and weighting factor γ=0.4; (2) SFC graph matching on MEC-NFV networks with a 100-node physical network, 20 VNFs, and equal utilization weights α=β=γ=1/3; and (3) multi-instance SFC mapping on Fog-to-Cloud (F2C) IoT environment with a 5-VNF chain across 5 nodes. These three algorithms have been evaluated under identical conditions: 10 independent runs, 200 iterations, 20–30 agents. Results demonstrate that GWO achieves the best VNF deployment objective (W =73.92, a 15.1% improvement over the BGWO baseline), PSO achieves the highest resource utilization (52.9%) in MEC-NFV placement, and both PSO and GWO reduce F2C end-to-end latency by 25% compared to the ILP reference (12 vs. 16 units at three instances), while all three algorithms reduce latency by approximately 80% relative to cloud-only deployment. PSO emerges as the most consistently high-performing algorithm across all three scenarios.
Chandrapal Singh Dangi, Sanjay Sharma· International Journal of Adv...· 0 citations
Energy remains the most critical and limiting resource in Wireless Sensor Networks (WSNs) and Internet of Things (IoT) systems, directly constraining network lifetime, scalability, and real-world deployability. Although multi-hop routing is widely adopted to reduce transmission energy and balance traffic load, recent solutions increasingly rely on metaheuristic optimization and machine learning techniques whose computational, control, and learning overhead is rarely accounted for. This leads to a fundamental energy–intelligence trade-off that challenges the sustainability of intelligent routing in resource-constrained environments. This paper presents a critical, energy-centric review of multi-hop routing approaches for IoT and WSNs proposed between 2018 and 2025. Heuristic, metaheuristic, dynamic and Heterogeneous routing, reinforcement learning, deep reinforcement learning, and explainable AI-based protocols are systematically analyzed with an emphasis on net energy efficiency, scalability, feasibility on constrained devices, and model realism, rather than reported performance gains alone. The analysis reveals that energy is predominantly treated as a secondary optimization objective rather than as a governing system constraint. To address this limitation, we propose a hybrid and explainable routing framework governed by energy awareness, in which intelligence activation is explicitly conditioned on its net energy benefit. This perspective provides a principled foundation for sustainable and trustworthy intelligent routing in next-generation IoT and WSN systems.
Moez Elarfaoui, Hamdi Ouechtati, Nadia Ben Azzouna· International Conference on...· 0 citations